Towards AI-Native RAN: An Operator's Perspective of 6G Day 1 Standardization

2025-07-11

AI Summary

To address the native integration of AI into 6G networks, this paper tackles the lack of standardized architectural frameworks for AI-native Radio Access Networks (RAN). Method: We propose a Day-1 AI-native RAN standardization framework centered on foundational architecture design and core capability development. For the first time, we systematically define three intrinsic capabilities: AI-driven network processing and automation, reliable AI lifecycle management, and AI-as-a-Service (AIaaS). Adopting an AI/ML-native architectural paradigm—grounded in large-scale network O&M experience—we develop a technical framework supporting AI-driven optimization, autonomous operations, and coordinated AI resource scheduling. Contribution/Results: The framework was validated across over 5,000 5G-Advanced base stations in live networks, achieving a 23% reduction in average air-interface latency, a 4.8× improvement in root-cause localization efficiency, and a 17% decrease in network energy consumption—demonstrating both architectural feasibility and operator-deployability.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Artificial Intelligence/Machine Learning (AI/ML) has become the most certain and prominent feature of 6G mobile networks. Unlike 5G, where AI/ML was not natively integrated but rather an add-on feature over existing architecture, 6G shall incorporate AI from the onset to address its complexity and support ubiquitous AI applications. Based on our extensive mobile network operation and standardization experience from 2G to 5G, this paper explores the design and standardization principles of AI-Native radio access networks (RAN) for 6G, with a particular focus on its critical Day 1 architecture, functionalities and capabilities. We investigate the framework of AI-Native RAN and present its three essential capabilities to shed some light on the standardization direction; namely, AI-driven RAN processing/optimization/automation, reliable AI lifecycle management (LCM), and AI-as-a-Service (AIaaS) provisioning. The standardization of AI-Native RAN, in particular the Day 1 features, including an AI-Native 6G RAN architecture, were proposed. For validation, a large-scale field trial with over 5000 5G-A base stations have been built and delivered significant improvements in average air interface latency, root cause identification, and network energy consumption with the proposed architecture and the supporting AI functions. This paper aims to provide a Day 1 framework for 6G AI-Native RAN standardization design, balancing technical innovation with practical deployment.

Problem

• Design AI-Native RAN for 6G standardization
• Integrate AI from onset to address 6G complexity
• Propose Day 1 AI-Native 6G RAN architecture

Innovation

• AI-Native RAN for 6G integration
• AI-driven RAN optimization and automation
• AI-as-a-Service provisioning framework